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Unravelling the Big-Data Conundrum
The Big data buzz has been around since quite a long time but the mystery of deriving meaningful and deeper insights from the data lakes that holds huge volumes of data still remains unanswered. Ignoring this data comes with a huge cost proving to be an irreparable mistake and resulting in reduced customer satisfaction, losing them to competitors, reduced profitability and loss of market share. Enterprises are therefore investing heavily in big data solutions to uncover the hidden patterns in this data.
The evolving needs and rapid adoption of technology will demand big data solution approach to be a hybrid model of on premise and cloud in the future. However, today Big Data analytics solutions not only have to handle huge volumes of data to predict the future events but also possess quick processing power to handle such large pile of data with multiple traits. An ideal Big Data solution should be capable of addressing the various spectrums of business needs like operations, new products development, growth strategies, and other covert opportunities for businesses.
Big Data poses certain crucial challenges that need to be confronted in order to provide personalized experience to individual customers. These challenges range from capturing accurate and quality data (customer Interactions and preferences), analyzing and processing these data on real-time basis to extract meaningful results and visualize them using interactive dashboards to make outcome based decisions. Another challenge facing enterprises is the demand to continuously invest in storage capacities for this data which only acts as a band-aid solution.
To ensure success, Digital enterprises have been leveraging Big Data technologies to gain actionable insights for making data-driven decisions. However, expertise to entirely leverage these agile technologies is still in the nascent stage and it will take quite a while to develop such competency.
Below are the simple steps to follow for enterprises to make their Big Data initiatives a success:
- Embed Self-Discovery and Data Visualization tools in your Big Data environment
Preparing the Data to predict the future trends is the most crucial aspect especially if enterprises access the data through multiple sources such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), social media, transactional data etc. Digital enterprises should deploy smart analytical tools that present the data through intuitive dashboards to help understand the data patterns cogently.
- Leveraging In-memory analytics to speed up the analytical process
Enterprises should leverage In-memory analytics to speed-up the processes for analyzing large volumes of data more quickly and efficiently. The perfect blend of In-memory computing and data visualization technologies will help enterprises analyze a wide range of data types to derive actionable insights while enhancing the overall user experience.
- Frame Data governance standards to ensure efficient enterprise information management
Establishing effective data governance standards help to establish accountability and security across the entire information life cycle; capture, manage, preserve, store and deliver. Furthermore Data governance policies should ensure secure and reliable delivery of the right information to the right person at the right time, thereby enabling improved business decisions, operational efficiencies and healthy relations amongst stakeholders.
- Embrace cloud technologies to harness value from Big Data
Enterprises will continue to invest in cost effective and scalable cloud computing technologies to analyze their Big Data in order to derive deeper insights and yield better business results, as it simplifies the IT infrastructure whilst considerably reducing investments in Data centers. Additionally, adoption of cloud computing technologies offers flexibility, affordability and scalability required by enterprises to analyze their big data promptly and proficiently.
- Adopt Predictive analytics to determine future trends
Enterprises should build capabilities around predictive analytics to perform real-time analysis of their data so as to gain better insights and a deeper understanding of future trends. This could be used to monitor and measure the current business progress and also act as a guide providing corrective steps to achieve the desired business outcome.
It is also essential to consider that though Big Data continues to provide enterprises with infinite insights to examine and explore opportunities and market competition; it still remains a powerful weapon that needed to be managed properly by CIO’s to align the business processes in accordance with organization’s goal. Furthermore, advancements in Digital (SMAC) technologies will inspire leaders to come up with innovative Big Data and advanced analytical solutions featuring high performance In-memory computing. Enterprise will also need to adopt hybrid (On-premise and cloud) Big Data strategies in order to simplify and unlock the value from massive volumes of data. Hence, CIO’s should be open towards embracing Big Data innovations swiftly to remain technology ready to meet future challenges and stay ahead of the competition.